{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00bb3b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bf93ef4460>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Python 绘制 Sigmoid函数图像\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "x = np.arange(-5.,5., 0.1) # 加.表示这个数值的类型是个浮点数\n",
    "y = sigmoid(x)\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f61e37d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
